1. Loading, pre-processing, analyzing and visualizing data

ft <- read_fst("IIGF_ETH_ERA5_19952021_ALL.fst") 
summary(ft$GID_2)
##    Length     Class      Mode 
##    586920 character character
u_gid<-length(unique(ft$GID_2))
u_list<-unique(ft$GID_2)
G1c<-ft%>%filter(GID_2 == "ETH.8.14_1")
G1c<-G1c %>% mutate(month = month(date))
G1c<-G1c%>%group_by(month)%>%mutate(mean_temp_monthly=mean(tas))

Proceed following the same steps c. to f. for a random sample of 10 regions in the database and plot the results.

2. Running a regression analysis, testing and explaining the results

Run a panel regression in R (plm package